All previous methods require knowledge about the attributes.
In [1]}, human knowledge about the attribute is provided to train a reward model which is used to train a NLG model by reinforcement learning.
On the other hand, [2]} and [3]} fine-tuned NLG model on additional dataset with attribute label.
These methods usually create highly-relevant sentences but have the limitation of fixing the available attributes in advance.
In addition, PPLM-Discrim in [4]} also requires attribute label, but it only has to train a much smaller discriminator model.
Finally, PPLM-BoW in [4]} has to first construct a curated word list which is a list words that are highly related to a given attribute.
Although no fine-tuning is needed, obtaining a word list for any arbitrary attribute is definitely not a trivial task.
